Autonomous Drone Racing with an Opponent: A First Approach

Abstract

Drone racing is a popular sport where human pilots control their drones via radio frequency to fly at high speed through complex race tracks. The latter has motivated to pose the question: could an autonomous drone beat a human in a drone race. Thus, some works have dealt with the autonomous drone racing challenge; however, very few works have dealt with the case of a race when an opponent is present in the track. In this work, we present an initial approach to address the problem of autonomous navigation while considering the presence of an opponent in the race track. To address this problem, we present a compact Convolutional Neural Network that predicts flight commands from a set of camera images captured on the fly. This network has been trained by imitation; this is, the network learns from examples generated by a human pilot. It is important to highlight that there is no explicit gate detection or trajectory planning/tracking in our approach. We have carried out experiments in the Gazebo simulator in a racetrack where the autonomous drone will face an opponent on its way to the gate. Our results show that the network manages to pilot the drone to evade the opponent, and after the evasion, the drone gets on-track towards the gate.